Role Description Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. You will enjoy the flexibility to telecommute* from anywhere within the U.S. as you take on some tough challenges. - Assist in designing, developing, and supporting GenAI/LLM-enabled features under guidance, from prototype through production. - Implement and iterate on prompt engineering patterns (prompting, prompt chaining, structured outputs) and contribute to basic evaluation approaches (quality, safety, hallucination risk, latency). - Support agent-building efforts (tool use, multi-step workflows, orchestration) and help define goals, constraints, and guardrails for safe behavior. - Build and maintain backend services using Java + Spring Boot and REST APIs to expose AI capabilities to applications; integrate with Database and event streaming via Kafka where needed. - Contribute to UI/API integration work (as needed) using React to deliver end-to-end features. - Write clean, maintainable code in Java and/or Python, applying engineering best practices and participating in code reviews. - Create and maintain automated tests (e.g., JUnit) and contribute to CI quality gates (linting, unit/integration tests). - Package and deploy services using Docker and support runtime deployments on Kubernetes; contribute to CI/CD pipelines. - Monitor and troubleshoot services using logging/observability tools (e.g., Splunk), assist with incident triage, and document fixes/runbooks. - Follow Responsible AI and secure engineering practices, including safe handling of sensitive data and adherence to internal SDLC/AIDLC standards. - Use approved productivity tools such as GitHub Copilot and Gemini to improve development speed and quality while complying with security and data-handling guidelines. - Design, develop, and deploy AI-powered solutions to address complex business challenges with emphasis on responsible use of AI. Qualifications - Bachelor’s degree. - 1+ years of experience in software engineering or ML engineering building software components production. - 1+ years of experience in Java or Python, including ability to write readable, testable, maintainable code. - 1+ years of experience building or integrating REST APIs; familiarity with Spring Boot or similar backend framework. - 1+ years of experience using Git and collaborative development practices (branches, pull requests, code reviews). - 1+ years of experience with Basic test automation experience (e.g., JUnit or Python testing frameworks). - 6+ months of experience of MongoDB (or other NoSQL) and general data modeling/querying concepts. Requirements - Familiarity with event-driven architecture concepts and/or messaging systems such as Kafka (hands-on preferred). - Foundational knowledge of AI/ML concepts, with exposure to NLP and/or LLM-based applications (e.g., summarization, extraction, Q&A). - Exposure to GenAI patterns such as prompt engineering, RAG, and/or agent workflows; ability to evaluate outputs for correctness, safety, and consistency. - Familiarity with containerization (Docker) and basic understanding of deployment environments (cloud and/or Kubernetes), CI/CD fundamentals. - Strong communication, teamwork, and problem-solving skills; comfortable working in an Agile delivery model. Benefits - Comprehensive benefits package. - Incentive and recognition programs. - Equity stock purchase. - 401k contribution (all benefits are subject to eligibility requirements). Application Deadline This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.

Role Description We're looking for a Senior AI/ML Engineer to act as the bridge between data infrastructure and customer-facing AI products. You'll specialize in: - Building low-latency API layers - Production-grade RAG systems - Complex ingestion pipelines - Human-in-the-Loop workflows You'll be working alongside Data Engineers to turn raw data lakes into live AI features. Qualifications - Overall Experience: 7+ years in Backend Software Engineering and AI Application Engineering, including exposure to Distributed Systems - AI & RAG Integration: 2+ years engineering production-grade RAG pipelines, managing vector retrieval context, and implementing secure validation layers for LLMs - Prototyping & Collaboration: Proven track record working synchronously with Data Engineers to rapidly turn raw data lakes and streams into production-ready AI feature prototypes - Proficiency in Advanced API Design & GraphQL Architecture - Proficiency in RAG, Data Flows & Ingestion Pipelines - Proficiency in State Management & Human-in-the-Loop (HITL) Automation - Production fluency in Python - Working knowledge of C# (.NET Core), Java, or Node.js/TypeScript for enterprise ingestion systems Requirements - Token-aware pagination for GraphQL/REST endpoints (LLM context-safe) - Custom Model Context Protocol (MCP) server development - GraphQL schema implementation using Apollo Server or AWS AppSync - Amazon Bedrock APIs (foundational model invocation and chaining) - Knowledge Bases for Amazon Bedrock (chunking, metadata extraction, vector sync from S3) - Hybrid retrieval using OpenSearch/Elasticsearch, pgvector, and MemoryDB / Redis OSS - High-throughput ingestion workers for embedding and vector generation - Ingestion pipelines with Amazon SQS, MSK (Kafka), and log sources - Third-party SaaS analytics API integration (e.g., Pendo, Hotjar, Google Analytics) - Autonomous Bedrock Agents with action groups - Amazon Bedrock Guardrails (prompt injection blocking, PII redaction, safety alignment) - Stateful orchestration with AWS Step Functions or LangGraph - Durable HITL gating workflows (pause, persist state, resume on human approval) - Idempotent processing with automated state rollbacks and dead-letter queues (DLQ) - End-to-end event tracing with OpenTelemetry (oTel) and Datadog Benefits - Remote work - 13 floating holidays - 15 vacation days per year completed - Good working environment Company Description Every qualified candidate who meets the requirements outlined in the job description will be considered in this hiring process without distinction. Furthermore, Jalasoft is an equal opportunity employer. We wholeheartedly embrace our responsibility to make employment decisions without regard to race, age, marital or social status, national origin, disability, sex, gender identity or expression, or any other characteristic or group of candidates or employees unrelated to their qualifications and suitability for the position. Our management is committed to upholding this policy with respect.

Role Description We're looking for a Senior AI/ML Engineer to design and build the foundational AI layers, multi-step agentic workflows, and developer tooling for the broader engineering organization. You'll focus on the internal runtime for AI: - Implementing prompt frameworks - Enforcing deterministic structured outputs - Deploying production-grade Human-in-the-Loop state automations - Turning raw context into reliable, zero-hallucination agent-based actions Qualifications - Overall Experience: 7+ years in Backend Software Engineering and AI/ML Application Engineering - Agentic Architecture: 2+ years engineering multi-step LLM workflows, autonomous agent loops, and multi-agent orchestration frameworks in production - Developer Platform Enablement: experience building internal SDKs, tools, or structured pipeline abstractions used by other product engineering teams to ship AI features rapidly - Proficiency in Multi-Step Agentic Workflows & Tooling - Proficiency in Advanced Amazon Bedrock & Prompt Engineering - Proficiency in Structured Output Pipelines & Performance Ingestion - Proficiency in State Management, Automation Consistency & HITL - Production fluency in Python - Proficiency in TypeScript/Node.js for Vercel AI SDK and AWS edge workers Requirements - Vercel AI SDK, LangGraph, or LlamaIndex for agentic orchestration - Reliable tool-calling (function-calling) interfaces for LLM-driven internal system navigation - Short-term and long-term agent memory architectures with ultra-low-latency in-memory stores (Amazon MemoryDB / Redis OSS / Valkey) - Amazon Bedrock APIs (foundational model invocation and chaining for agent loops) - Programmatic prompt optimization, few-shot prompting, dynamic context injection, and prompt caching - Amazon Bedrock Guardrails (safety gating, prompt injection mitigation, PII redaction) - Type-safe schema enforcement with Pydantic (Python) or Zod (TypeScript) - Ingestion workers consuming Amazon MSK (Kafka), SQS, and log sources - Optimistic Concurrency Control (OCC) and async write-delegation to prevent PostgreSQL deadlocks - Deterministic multi-step pipelines with AWS Step Functions or LangGraph - Durable HITL workflows (pause prior to DB writes or API transactions, persist state, resume on approval) - Idempotent execution layers for safe agent loop retries without record duplication - Working knowledge of C# (.NET Core) or Java for enterprise pipeline integrations Benefits - Remote work - 13 floating holidays - 15 vacation days per year completed - Good working environment Company Description Every qualified candidate who meets the requirements outlined in the job description will be considered in this hiring process without distinction. Furthermore, Jalasoft is an equal opportunity employer. We wholeheartedly embrace our responsibility to make employment decisions without regard to race, age, marital or social status, national origin, disability, sex, gender identity or expression, or any other characteristic or group of candidates or employees unrelated to their qualifications and suitability for the position. Our management is committed to upholding this policy with respect.

• Mapear oportunidades em crédito e cobrança onde agentes de IA podem substituir ou ampliar processos manuais, considerando fluxos de trabalho, riscos operacionais e critérios de sucesso em ambientes regulados • Projetar e manter arquiteturas de **Retrieval Augmented Generation (RAG)**, alimentando agentes com informações precisas e atualizadas • Gerenciar pipelines de ingestão de dados não estruturados (PDFs, áudios, textos, imagens), incluindo OCR e extração de entidades • Desenvolver e orquestrar sistemas multi-agente utilizando frameworks como **LangGraph, CrewAI ou AutoGen** • Implementar padrões arquiteturais como **ReAct, Plan-and-Execute e Reflexion** • Integrar agentes com APIs internas, sistemas legados e fontes de dados de crédito e cobrança • Estruturar e versionar prompts de sistema, instrução e contexto com técnicas avançadas (**chain-of-thought, few-shot prompting, structured outputs, self-consistency**), garantindo comportamentos determinísticos e auditáveis • Avaliar a qualidade das respostas dos agentes com métricas especializadas (**Faithfulness, Answer Relevancy, Context Precision, Hallucination Rate**) • Implementar **guardrails** e camadas de segurança para prevenção de alucinações, prompt injection e vazamento de dados sensíveis, em conformidade com a LGPD • Operar pipelines de **LLMOps** para monitoramento contínuo de custos, latência, qualidade e drift comportamental dos agentes em produção • Garantir versionamento de prompts, modelos e bases de conhecimento com rastreabilidade completa • Acompanhar o desempenho dos agentes em produção, iterando sobre base de conhecimento, lógica de orquestração e estratégias de fallback • Medir o impacto em KPIs de negócio (redução de tempo de análise de crédito, aumento na taxa de recuperação, melhoria na experiência do cliente) • Elaborar documentação técnica conforme padrões internos e exigências regulatórias, garantindo auditabilidade das decisões automatizadas